-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\knm4642\Dropbox\Nha Meo Heo Beo\_JMP\WORKING FOLDER\Table A11_Effect across industries and sizes.log
  log type:  text
 opened on:  18 Feb 2026, 23:52:36

. 
. //PREPARE DATA
. use "Baseline CEO firm year sample.dta", clear

. * construct additional variables
. gen pharmachem = inlist(sector, ///
>         "Chemicals", ///
>         "Pharmaceuticals and Biotechnology", ///
>         "Health")

. gen electroit = inlist(sector, ///
>         "Electronic & Electrical Equipment", ///
>         "Information Technology Hardware", ///
>         "Software & Computer Services")

. gen timesinceCEO = min(year - termstartyr, 9)

. * set globals
. global firm = "firmage firmage2"

. global ceo = "gender age age2 yrinco i.education"

. global sample = "year < 2012 & !nonUS"

. global cluster = "mainethcode"

. 
. 
. //PREPARE TABLE
. eststo clear

. eststo col1: /// pharma/chemical industries
>         reghdfe ash_f1allpat trust_sd ${firm} ${ceo} ///
>         if ${sample} & pharmachem, a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      5,234
Absorbing 2 HDFE groups                           F(  10,     31) =      42.10
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8643
                                                  Adj R-squared   =     0.8435
                                                  Within R-sq.    =     0.0057
Number of clusters (mainethcode) =         32     Root MSE        =     0.6327

                           (Std. err. adjusted for 32 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .0764197   .0347839     2.20   0.036     .0054775     .147362
     firmage |  -.0172272   .0083118    -2.07   0.047    -.0341792   -.0002752
    firmage2 |   .0003006   .0002321     1.29   0.205    -.0001728     .000774
      gender |   .0923159   .1447565     0.64   0.528    -.2029168    .3875487
         age |    -.00867   .0204684    -0.42   0.675    -.0504156    .0330755
        age2 |   .0000599   .0001785     0.34   0.740    -.0003042    .0004239
      yrinco |   .0091887   .0019874     4.62   0.000     .0051353    .0132421
             |
   education |
          2  |   .2236319   .1218529     1.84   0.076    -.0248887    .4721525
          3  |   .2246486   .1531917     1.47   0.153    -.0877878    .5370851
          4  |   .2316377   .1271538     1.82   0.078    -.0276941    .4909695
             |
       _cons |   1.027364   .5515465     1.86   0.072    -.0975228     2.15225
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       675           0         675     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Pharma/chem"

added macro:
             e(sample) : "Pharma/chem"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |       5234        675

.                 eststo col1, add(nofirms r(ndistinct))
(e(nofirms) = 675 added)

. eststo col2: /// pharma/chemical industries, by tenure as CEO
>         reghdfe ash_f1allpat c.trust_sd##c.timesinceCEO ${firm} ${ceo} ///
>         if ${sample} & pharmachem, a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      5,234
Absorbing 2 HDFE groups                           F(  12,     31) =      52.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8645
                                                  Adj R-squared   =     0.8437
                                                  Within R-sq.    =     0.0073
Number of clusters (mainethcode) =         32     Root MSE        =     0.6323

                                        (Std. err. adjusted for 32 clusters in mainethcode)
-------------------------------------------------------------------------------------------
                          |               Robust
             ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                 trust_sd |    .012086   .0492203     0.25   0.808    -.0882996    .1124715
             timesinceCEO |  -.1199402   .0709922    -1.69   0.101    -.2647297    .0248493
                          |
c.trust_sd#c.timesinceCEO |   .0253066   .0141797     1.78   0.084    -.0036131    .0542262
                          |
                  firmage |  -.0177001   .0082774    -2.14   0.040    -.0345819   -.0008183
                 firmage2 |   .0002823   .0002283     1.24   0.226    -.0001834    .0007479
                   gender |   .0911292   .1411923     0.65   0.523    -.1968344    .3790929
                      age |  -.0133412   .0228766    -0.58   0.564    -.0599983     .033316
                     age2 |   .0000936   .0001941     0.48   0.633    -.0003022    .0004894
                   yrinco |   .0071584   .0027858     2.57   0.015     .0014768      .01284
                          |
                education |
                       2  |   .2414449   .1307601     1.85   0.074    -.0252421    .5081318
                       3  |   .2371092   .1596455     1.49   0.148      -.08849    .5627083
                       4  |   .2468957   .1365825     1.81   0.080    -.0316662    .5254577
                          |
                    _cons |   1.494101   .7024359     2.13   0.041     .0614731    2.926728
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       675           0         675     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Pharma/chem"

added macro:
             e(sample) : "Pharma/chem"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |       5234        675

.                 eststo col2, add(nofirms r(ndistinct))
(e(nofirms) = 675 added)

. eststo col3: /// ICT/electronic industries
>         reghdfe ash_f1allpat trust_sd ${firm} ${ceo} ///
>         if ${sample} & electroit, a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      7,274
Absorbing 2 HDFE groups                           F(  10,     30) =       8.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8989
                                                  Adj R-squared   =     0.8850
                                                  Within R-sq.    =     0.0029
Number of clusters (mainethcode) =         31     Root MSE        =     0.6591

                           (Std. err. adjusted for 31 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .0950543   .0456579     2.08   0.046     .0018084    .1883002
     firmage |  -.0129679   .0094036    -1.38   0.178    -.0321726    .0062368
    firmage2 |   .0001224   .0001609     0.76   0.453    -.0002063     .000451
      gender |   .0276968   .0708727     0.39   0.699    -.1170445    .1724382
         age |  -.0153179   .0243381    -0.63   0.534    -.0650231    .0343872
        age2 |   .0001123   .0002099     0.53   0.597    -.0003164    .0005411
      yrinco |   .0053209   .0019325     2.75   0.010     .0013743    .0092676
             |
   education |
          2  |   .0855322   .0885636     0.97   0.342    -.0953388    .2664033
          3  |   .1211833   .0730928     1.66   0.108     -.028092    .2704587
          4  |   .1243405    .064279     1.93   0.063    -.0069348    .2556157
             |
       _cons |   1.557039   .6456654     2.41   0.022     .2384145    2.875664
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       862           0         862     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "ICT/elec"

added macro:
             e(sample) : "ICT/elec"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |       7274        862

.                 eststo col3, add(nofirms r(ndistinct))
(e(nofirms) = 862 added)

. eststo col4: /// ICT/electronic industries, by tenure as CEO
>         reghdfe ash_f1allpat c.trust_sd##c.timesinceCEO ${firm} ${ceo} ///
>         if ${sample} & electroit, a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      7,274
Absorbing 2 HDFE groups                           F(  12,     30) =      11.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8991
                                                  Adj R-squared   =     0.8851
                                                  Within R-sq.    =     0.0046
Number of clusters (mainethcode) =         31     Root MSE        =     0.6587

                                        (Std. err. adjusted for 31 clusters in mainethcode)
-------------------------------------------------------------------------------------------
                          |               Robust
             ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                 trust_sd |   .1373812   .0534441     2.57   0.015     .0282337    .2465287
             timesinceCEO |   .0823826   .0633252     1.30   0.203    -.0469448    .2117099
                          |
c.trust_sd#c.timesinceCEO |  -.0137299   .0123675    -1.11   0.276    -.0389877    .0115278
                          |
                  firmage |  -.0136888   .0099564    -1.37   0.179    -.0340226     .006645
                 firmage2 |   .0000937   .0001745     0.54   0.595    -.0002626      .00045
                   gender |   .0294794   .0677229     0.44   0.666    -.1088291     .167788
                      age |  -.0206791   .0250935    -0.82   0.416    -.0719269    .0305687
                     age2 |    .000154   .0002161     0.71   0.482    -.0002874    .0005954
                   yrinco |   .0019022   .0018423     1.03   0.310    -.0018603    .0056646
                          |
                education |
                       2  |    .082106   .0874016     0.94   0.355    -.0963918    .2606039
                       3  |   .1184273   .0704646     1.68   0.103    -.0254807    .2623353
                       4  |   .1121091     .06269     1.79   0.084    -.0159209    .2401392
                          |
                    _cons |   1.514269   .6964487     2.17   0.038     .0919306    2.936607
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       862           0         862     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "ICT/elec"

added macro:
             e(sample) : "ICT/elec"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |       7274        862

.                 eststo col4, add(nofirms r(ndistinct))
(e(nofirms) = 862 added)

. eststo col5: /// other industries
>         reghdfe ash_f1allpat trust_sd ${firm} ${ceo} ///
>         if ${sample} & !(pharmachem | electroit), a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     16,876
Absorbing 2 HDFE groups                           F(  10,     34) =      11.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9028
                                                  Adj R-squared   =     0.8892
                                                  Within R-sq.    =     0.0013
Number of clusters (mainethcode) =         35     Root MSE        =     0.4464

                           (Std. err. adjusted for 35 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .0364234   .0158662     2.30   0.028     .0041794    .0686674
     firmage |  -.0045417   .0011107    -4.09   0.000    -.0067989   -.0022845
    firmage2 |  -9.41e-06   .0000415    -0.23   0.822    -.0000937    .0000748
      gender |  -.0590593   .0435905    -1.35   0.184    -.1476458    .0295271
         age |   .0070142   .0053848     1.30   0.201     -.003929    .0179574
        age2 |  -.0000731   .0000457    -1.60   0.119     -.000166    .0000199
      yrinco |    .002241   .0007688     2.91   0.006     .0006785    .0038034
             |
   education |
          2  |   .0011714   .0313914     0.04   0.970    -.0626235    .0649663
          3  |   .0048761   .0346052     0.14   0.889    -.0654501    .0752023
          4  |   .0395987   .0361348     1.10   0.281     -.033836    .1130335
             |
       _cons |   .4096941   .1522902     2.69   0.011     .1002031    .7191851
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      2061           0        2061     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Others"

added macro:
             e(sample) : "Others"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      16876       2061

.                 eststo col5, add(nofirms r(ndistinct))
(e(nofirms) = 2061 added)

. gen sizepct = sizepct_at_sic3
(11,630 missing values generated)

. eststo col6: /// by asset size quintile
>         reghdfe ash_f1allpat c.trust_sd#i.sizepct i.sizepct ${firm} ${ceo} ///
>         if ${sample}, a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  18,     37) =      73.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9019
                                                  Adj R-squared   =     0.8881
                                                  Within R-sq.    =     0.0078
Number of clusters (mainethcode) =         38     Root MSE        =     0.5412

                                 (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------------
                   |               Robust
      ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
sizepct#c.trust_sd |
                1  |   .0317463   .0351526     0.90   0.372    -.0394797    .1029723
                2  |   .0243819   .0302843     0.81   0.426    -.0369799    .0857436
                3  |   .1290495   .0413877     3.12   0.004       .04519    .2129091
                4  |   .0744401   .0393688     1.89   0.066    -.0053286    .1542088
                5  |   .0644141   .0218093     2.95   0.005     .0202242     .108604
                   |
           sizepct |
                2  |   .0606652   .2165288     0.28   0.781    -.3780639    .4993942
                3  |  -.4100354   .3282665    -1.25   0.219    -1.075166    .2550958
                4  |   -.052349   .3479335    -0.15   0.881    -.7573292    .6526313
                5  |   .1301711   .1888049     0.69   0.495     -.252384    .5127262
                   |
           firmage |  -.0101271   .0020554    -4.93   0.000    -.0142918   -.0059624
          firmage2 |   .0001393   .0000456     3.05   0.004     .0000468    .0002317
            gender |  -.0056624   .0318247    -0.18   0.860    -.0701453    .0588205
               age |  -.0025695   .0105318    -0.24   0.809     -.023909      .01877
              age2 |   6.92e-06   .0000864     0.08   0.937    -.0001681     .000182
            yrinco |    .003736   .0005932     6.30   0.000     .0025341    .0049379
                   |
         education |
                2  |   .0452642   .0500106     0.91   0.371    -.0560669    .1465953
                3  |   .0613631   .0348522     1.76   0.087    -.0092542    .1319805
                4  |   .0847017   .0435011     1.95   0.059    -.0034399    .1728432
                   |
             _cons |   .8580843   .3064258     2.80   0.008     .2372067    1.478962
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Full"

added macro:
             e(sample) : "Full"

.                 estadd local sizepct    "By asset"

added macro:
            e(sizepct) : "By asset"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col6, add(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. replace sizepct = sizepct_emp_sic3
(17,926 real changes made, 1,008 to missing)

. eststo col7: /// by employment size quintile
>         reghdfe ash_f1allpat c.trust_sd#i.sizepct i.sizepct ${firm} ${ceo} ///
>         if ${sample}, a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     28,713
Absorbing 2 HDFE groups                           F(  18,     37) =      68.02
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9035
                                                  Adj R-squared   =     0.8897
                                                  Within R-sq.    =     0.0077
Number of clusters (mainethcode) =         38     Root MSE        =     0.5395

                                 (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------------
                   |               Robust
      ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
sizepct#c.trust_sd |
                1  |   .0262342   .0366639     0.72   0.479    -.0480539    .1005223
                2  |   .0444934   .0321323     1.38   0.174    -.0206128    .1095995
                3  |   .1383447    .027619     5.01   0.000     .0823833    .1943061
                4  |    .035871    .034898     1.03   0.311    -.0348391    .1065812
                5  |   .0666881   .0302683     2.20   0.034     .0053587    .1280175
                   |
           sizepct |
                2  |  -.0663216   .2367186    -0.28   0.781    -.5459591    .4133158
                3  |  -.4892006   .2559362    -1.91   0.064    -1.007777    .0293753
                4  |   .1469701   .2740225     0.54   0.595    -.4082521    .7021924
                5  |   .0748985   .2345067     0.32   0.751    -.4002573    .5500543
                   |
           firmage |  -.0099477    .001875    -5.31   0.000    -.0137468   -.0061486
          firmage2 |   .0001523   .0000458     3.33   0.002     .0000595    .0002451
            gender |  -.0088909   .0341619    -0.26   0.796    -.0781095    .0603277
               age |  -.0043681   .0107664    -0.41   0.687     -.026183    .0174467
              age2 |     .00002   .0000893     0.22   0.824    -.0001609    .0002009
            yrinco |   .0038528   .0006894     5.59   0.000     .0024558    .0052497
                   |
         education |
                2  |   .0545528   .0555735     0.98   0.333    -.0580497    .1671554
                3  |     .07076   .0401755     1.76   0.086    -.0106433    .1521633
                4  |   .0910382   .0482421     1.89   0.067    -.0067095     .188786
                   |
             _cons |   .9313842   .3000881     3.10   0.004     .3233479     1.53942
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3577           0        3577     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Full"

added macro:
             e(sample) : "Full"

.                 estadd local sizepct    "By emp"

added macro:
            e(sizepct) : "By emp"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      28713       3577

.                 eststo col7, add(nofirms r(ndistinct))
(e(nofirms) = 3577 added)

. replace sizepct = sizepct_at_sic3haspat
(33,302 real changes made, 22,781 to missing)

. eststo col8: /// by asset size quintile during patent period
>         reghdfe ash_f1allpat c.trust_sd#i.sizepct i.sizepct ${firm} ${ceo} ///
>         if ${sample} & haspat & inrange(year, firstpatyear-1, lastpatyear), ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     14,881
Absorbing 2 HDFE groups                           F(  18,     34) =      14.82
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8588
                                                  Adj R-squared   =     0.8356
                                                  Within R-sq.    =     0.0094
Number of clusters (mainethcode) =         35     Root MSE        =     0.7296

                                 (Std. err. adjusted for 35 clusters in mainethcode)
------------------------------------------------------------------------------------
                   |               Robust
      ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
sizepct#c.trust_sd |
                1  |   .0874913   .0324625     2.70   0.011     .0215197     .153463
                2  |   .1321943   .0636749     2.08   0.046     .0027913    .2615972
                3  |   .1593386   .0722956     2.20   0.034     .0124162    .3062609
                4  |   .0561184   .0532012     1.05   0.299    -.0519995    .1642362
                5  |   .0673968   .0435113     1.55   0.131    -.0210289    .1558224
                   |
           sizepct |
                2  |  -.1889353   .3783702    -0.50   0.621    -.9578761    .5800056
                3  |   -.208745    .435845    -0.48   0.635    -1.094489    .6769986
                4  |   .4030312   .3953004     1.02   0.315    -.4003158    1.206378
                5  |   .4833635   .2943731     1.64   0.110    -.1148746    1.081602
                   |
           firmage |  -.0114384   .0072931    -1.57   0.126    -.0262598     .003383
          firmage2 |   .0002698   .0000824     3.27   0.002     .0001023    .0004372
            gender |  -.0813105   .0586474    -1.39   0.175    -.2004964    .0378755
               age |  -.0027852    .016715    -0.17   0.869    -.0367543    .0311838
              age2 |  -.0000132   .0001346    -0.10   0.922    -.0002868    .0002604
            yrinco |   .0058631   .0013063     4.49   0.000     .0032083    .0085178
                   |
         education |
                2  |   .1200046   .1043923     1.15   0.258    -.0921461    .3321553
                3  |   .1364912   .0833048     1.64   0.111    -.0328046     .305787
                4  |   .1799403   .0843617     2.13   0.040     .0084967    .3513839
                   |
             _cons |   1.496841   .5084795     2.94   0.006     .4634861    2.530196
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      2068           0        2068     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Pat period"

added macro:
             e(sample) : "Pat period"

.                 estadd local sizepct    "By asset"

added macro:
            e(sizepct) : "By asset"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      14881       2068

.                 eststo col8, add(nofirms r(ndistinct))
(e(nofirms) = 2068 added)

. replace sizepct = sizepct_emp_sic3haspat
(7,571 real changes made, 420 to missing)

. eststo col9: /// by employment size quintile during patent period
>         reghdfe ash_f1allpat c.trust_sd#i.sizepct i.sizepct ${firm} ${ceo} ///
>         if ${sample} & haspat & inrange(year, firstpatyear-1, lastpatyear), ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     14,586
Absorbing 2 HDFE groups                           F(  18,     34) =      52.82
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8612
                                                  Adj R-squared   =     0.8381
                                                  Within R-sq.    =     0.0105
Number of clusters (mainethcode) =         35     Root MSE        =     0.7260

                                 (Std. err. adjusted for 35 clusters in mainethcode)
------------------------------------------------------------------------------------
                   |               Robust
      ash_f1allpat | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
sizepct#c.trust_sd |
                1  |   .0914568   .0553537     1.65   0.108    -.0210354     .203949
                2  |   .1545581   .0570622     2.71   0.011     .0385938    .2705223
                3  |   .1677953   .0527517     3.18   0.003     .0605911    .2749996
                4  |    .061477   .0536488     1.15   0.260    -.0475504    .1705044
                5  |   .0124766   .0480442     0.26   0.797     -.085161    .1101143
                   |
           sizepct |
                2  |  -.2086736   .3950352    -0.53   0.601    -1.011482    .5941344
                3  |  -.2067539   .4782345    -0.43   0.668    -1.178643    .7651356
                4  |   .4993063   .3898826     1.28   0.209    -.2930306    1.291643
                5  |   .7994272   .4584848     1.74   0.090     -.132326     1.73118
                   |
           firmage |  -.0116237   .0049325    -2.36   0.024    -.0216477   -.0015996
          firmage2 |   .0002817   .0000819     3.44   0.002     .0001153    .0004482
            gender |   -.082933    .057403    -1.44   0.158    -.1995899    .0337239
               age |  -.0040927   .0174176    -0.23   0.816    -.0394895    .0313042
              age2 |  -3.83e-06   .0001424    -0.03   0.979    -.0002933    .0002856
            yrinco |   .0059235   .0013506     4.39   0.000     .0031789    .0086682
                   |
         education |
                2  |   .1525475    .120432     1.27   0.214    -.0921997    .3972947
                3  |   .1669251   .0962018     1.74   0.092    -.0285805    .3624307
                4  |   .2102206   .1016825     2.07   0.046     .0035769    .4168644
                   |
             _cons |   1.452248   .6198933     2.34   0.025     .1924736    2.712023
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      2054           0        2054     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 estadd local sample     "Pat period"

added macro:
             e(sample) : "Pat period"

.                 estadd local sizepct    "By emp"

added macro:
            e(sizepct) : "By emp"

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      14586       2054

.                 eststo col9, add(nofirms r(ndistinct))
(e(nofirms) = 2054 added)

. 
. global coeflist1 = "trust_sd c.trust_sd#c.timesinceCEO"

. global coeflist2 = "1.sizepct#c.trust_sd 2.sizepct#c.trust_sd 3.sizepct#c.trust_sd 4.sizepct#c.trust_sd 5.sizepct#c.trust_sd"

. esttab /*using "Table A11_Effect across industries and sizes.tex"*/, ///
>         cells(b(star fmt(3)) se(par fmt(3))) starlevels(* 0.1 ** 0.05 *** 0.01) ///
>         nomtitle collabels(none) label varwidth(30) modelwidth(11) ///
>         mgroups("arsinh(Future patent applications)", pattern(1 0 0 0 0 0 0 0 0) ///
>                 prefix(\multicolumn{@span}{c}{) suffix(}) span) ///
>         keep(${coeflist1} ${coeflist2}) order(${coeflist1} ${coeflist2}) ///
>         coeflab(trust_sd "CEO's trust" ///
>                         c.trust_sd#c.timesinceCEO "Trust $\times$ Tenure as CEO" ///
>                         1.sizepct#c.trust_sd "Trust $\times$ Size quintile 1" ///
>                         2.sizepct#c.trust_sd "Trust $\times$ Size quintile 2" ///
>                         3.sizepct#c.trust_sd "Trust $\times$ Size quintile 3" ///
>                         4.sizepct#c.trust_sd "Trust $\times$ Size quintile 4" ///
>                         5.sizepct#c.trust_sd "Trust $\times$ Size quintile 5") ///
>         stats(sample sizepct FE controls N nofirms, fmt( %9.3fc %9.3fc %9.0fc %9.0fc) ///
>                 lab("Sample" "Firm size quintile" "Firm \& Year FEs" "Baseline controls" "Observations" "Firms"))

---------------------------------------------------------------------------------------------------------------------------------------------------------------------
                               \multicolumn{9}{c}{arsinh(Future patent applications)}                                                                                
                                       (1)            (2)            (3)            (4)            (5)            (6)            (7)            (8)            (9)   
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
CEO's trust                          0.076**        0.012          0.095**        0.137**        0.036**                                                             
                                   (0.035)        (0.049)        (0.046)        (0.053)        (0.016)                                                               
Trust $\times$ Tenure as CEO                        0.025*                       -0.014                                                                              
                                                  (0.014)                       (0.012)                                                                              
Trust $\times$ Size quintile 1                                                                                  0.032          0.026          0.087**        0.091   
                                                                                                              (0.035)        (0.037)        (0.032)        (0.055)   
Trust $\times$ Size quintile 2                                                                                  0.024          0.044          0.132**        0.155** 
                                                                                                              (0.030)        (0.032)        (0.064)        (0.057)   
Trust $\times$ Size quintile 3                                                                                  0.129***       0.138***       0.159**        0.168***
                                                                                                              (0.041)        (0.028)        (0.072)        (0.053)   
Trust $\times$ Size quintile 4                                                                                  0.074*         0.036          0.056          0.061   
                                                                                                              (0.039)        (0.035)        (0.053)        (0.054)   
Trust $\times$ Size quintile 5                                                                                  0.064***       0.067**        0.067          0.012   
                                                                                                              (0.022)        (0.030)        (0.044)        (0.048)   
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sample                         Pharma/chem    Pharma/chem       ICT/elec       ICT/elec         Others           Full           Full     Pat period     Pat period   
Firm size quintile                                                                                           By asset         By emp       By asset         By emp   
Firm \& Year FEs                         X              X              X              X              X              X              X              X              X   
Baseline controls                        X              X              X              X              X              X              X              X              X   
Observations                         5,234          5,234          7,274          7,274         16,876         29,384         28,713         14,881         14,586   
Firms                                  675            675            862            862          2,061          3,598          3,577          2,068          2,054   
---------------------------------------------------------------------------------------------------------------------------------------------------------------------

. 
. cap log close
